考虑电池包数量的自动化集装箱码头多AGV调度优化

针对自动化集装箱码头自动引导小车（automated guided vehicle，AGV）的实际换电特性，为了降低AGV的总任务完成时间和换电总时间，合理规划换电站内的电池包数量，建立了双层规划模型。首先考虑AGV的电池续航、空重载SOC变化特性和不同剩余电量与速度变化，以降低AGV的总任务完成时间为目标，构建考虑换电的多AGV集装箱任务调度上层模型。在此基础上，为了合理规划换电站内的电池包数量，考虑自动化码头中换电站的实际电池包选取原则和换电流程，对换电站和电池包的选择进行决策，以降低换电总时间为目标，构建换电电池包配置下层模型。最后通过遗传算法分别对小规模和大规模算例进行求解。算例结果表明，此双层规划模型能够有效地减少总任务完成时间和换电总时间，提高了6.46%的AGV利用率，减少了23.1%的换电站电池包数量。

Research on multi AGV scheduling optimization of automated container terminal considering number of battery packs

According to the actual power exchange characteristics of AGV in automated container terminal, this paper built a two-level programming model to reduce the total task completion time and power exchange time of the AGV, and reasonably planed the number of battery packs in the power exchange station. Firstly, considering the battery life, air and heavy load SOC variation characteristics and different residual power and speed changes of AGV, in order to reduce the total task completion time of AGV, it built an upper-level model for multi-AGV container task scheduling considering power exchange. On this basis, in order to reasonably plan the number of battery packs in the power exchange station, considered the actual battery pack selection principle and power exchange process of the power exchange station in the automatic wharf, made decisions on the selection of power exchange station and battery pack, and built the lower level model of power exchange battery pack configuration with the goal of reducing the total power exchange time. Finally, it solved small-scale and large-scale examples by genetic algorithm. The example results show that the two-level programming model can effectively reduce the total task completion time and the total power exchange time, improve the utilization rate of AGV by 6.46% and reduce the number of battery packs in the power exchange station by 23.1%.